About Pasqal
PASQAL designs and develops Quantum Processing Units (QPUs) and associated software tools.
Our innovative technology enables us to address use cases that are currently beyond the reach of the most powerful supercomputers; these cases can concern industrial application challenges as well as fundamental science needs.
In addition to the exceptional computing power they provide, QPUs are highly energy efficient and will contribute to a significant reduction in the carbon footprint of the HPC industry.
Job Description
As part of our strong growth, we are looking for an engineer with a strong background in Operations Research (Applied Mathematics), including exact and heuristic algorithms and graph theory, to reinforce our Quantum Graph Optimization (QGO) team.
In this role, you will primarily focus on designing, developing, and delivering client-facing projects based on PASQAL quantum processors. You will adapt existing solutions and co-develop new applications with clients, translating real-world optimization problems into graph-based and quantum-ready formulations. Your work will be strongly oriented toward practical use cases in graph theory, operations research, and related industrial applications.
You will act as a technical lead for client engagements, managing the technical relationship with existing customers and partners and leading applied R&D projects with prospective ones. This includes collaborating closely with clients and partners to define requirements, prototype solutions based on our quantum technology, and deliver results, as well as contributing to internal development efforts. You will work in close coordination with Engineering, R&D, and external partners across all project phases, from problem definition to deployment.
As a Senior Quantum Algorithm Developer, your missions and responsibilities will be as follows:
- Investigate realistic implementations in our neutral-atom hardware, mostly based on analog paradigms, testing realistic parameter settings and analyzing the limitations and potential of our technology in solving challenging, industrial optimization problems
- Collaborate with our industrial partners, and in some projects also work together with clients who possess domain-expertise on the use cases we work.
- Apply analytical tools in mathematics and computer science, to test the feasibility of the algorithmic methods in solving some of the most promising use cases.
- Investigate the risk and return for certain use cases, the limitations of classical computing in solving them, and the potential benefits of our quantum computers.
- Develop codes in collaboration with our software engineering team, running on a variety of special-purpose simulation/emulation backends locally, on our HPC cluster, and mostly using our QPUs.
- Provide help on parts of code for which they are not responsible if necessary
- Contribute to the activities of the team pertaining to Quantum Optimization, notably in the form of developing new algorithms and scientific watch
- Have an inventive activity in the scientific and technical fields related to the Company's research, products, technologies and markets; filing patents.
About you
With a MSc or a PhD in Operations Research or in a related field and with at least 5 years in a similar position, you have the following assets:
- Experience with at least one of the following optimization frameworks: SCIP, DIP, PuLP, DipPy, Pyomo, JuMP
- Experience with at least one of the following programming languages: C, C++, Python, CUDA, Julia
- Experience with linear and non-linear solvers such as Cplex, Baron, Gurobi, GLPK, IPOP
- Strong taste for Applied Mathematics and graphs, and a keen interest in deep tech and new technologies
- Good practices in algorithms development and numerical simulations
- Good practices in research and project management
- Report/documentation writing
Notions of quantum computing, atomic physics, and optics are not mandatory but highly appreciated if combined with the aforementioned skills.
What we offer
- Offices in Saudi Arabia - Riyadh (KACST), with travels to Dammam and Jeddah to be expected
- Travels to our HQ (Palaiseau, France) 1 week every 2 to 3 months
- A flexible rhythm of remote work (2 days per week)
- Type of contract: permanent
- A dynamic and close-knit international team
- A key role in a growing start-up
- Free time to train and go to conferences once a year
Recruitment process
- An interview with our People Acquisition Partner (~45').
- A technical interview with the Lead of the Quantum Optimization team (~1hr)
- A home assignment
- A team fit interview with a few people from the Optimization team (~2hrs - onsite or online)
- An offer
PASQAL is an equal opportunity employer. We are committed to creating a diverse and inclusive workplace, as inclusion and diversity are essential to achieving our mission. We encourage applications from all qualified candidates, regardless of gender, ethnicity, age, religion or sexual orientation.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The Senior Quantum Algorithm Developer role addresses the critical application layer gap between developing neutral-atom quantum computing hardware and delivering commercially viable solutions for industrial optimization challenges. This position is structurally necessary to translate abstract quantum graph optimization methodologies into client-facing proofs-of-concept, establishing the technical viability of quantum processing units (QPUs) for complex, real-world problems. The value-chain impact resides in reducing the Technology Readiness Level (TRL) mismatch between emerging hardware and enterprise adoption by providing hybrid, quantum-enabled software stacks anchored in operations research and applied mathematics. Effective execution accelerates market readiness by defining benchmarks where quantum advantage can be demonstrated beyond classical supercomputing limitations.
The quantum applications segment of the value chain currently faces constraints centered on translational expertise—the ability to map known complex optimization problems (e.g., logistics, finance, routing) onto nascent QPU architectures, particularly analog quantum simulation paradigms. Talent scarcity remains acute at this interface, demanding hybrid professionals fluent in both classical optimization tools (such as Gurobi or CPLEX) and low-level quantum hardware constraints. This synthesis role is vital in the regional context, where massive industrial sectors are actively seeking disruptive computational capabilities to manage large-scale resource allocation and logistics. The expansion of quantum computing infrastructure into high-performance computing (HPC) centers necessitates algorithm developers who can architect hybrid classical-quantum workflows that effectively manage the noise and limited qubit count inherent in current generation neutral-atom systems. Furthermore, global investment trends indicate a market shift from pure research toward validated application delivery, placing optimization specialists at the center of commercial viability assessments and strategic customer engagements, particularly where graph theory can address intractable industrial computation needs. Sector-wide efforts continue to address talent and integration challenges in quantum systems.
The core technical architecture for this function synthesizes traditional Operations Research (OR) expertise with the specialized requirements of quantum graph formulation. Mastery is required across classical optimization solvers (e.g., Cplex, GLPK, SCIP), robust implementation languages (Python, C++), and the ability to leverage HPC environments for large-scale problem simulation and emulation. Capability extends to defining the precise mapping from high-level optimization objectives—such as minimizing costs or maximizing throughput—down to the specific analog control pulses and Hamiltonian engineering suitable for neutral-atom hardware platforms. This structural enablement layer allows for the systematic benchmarking of quantum algorithms (like QAOA or specific analog simulations) against established classical heuristics. Effective application of analytical tools in graph theory ensures problems are correctly decomposed, minimizing required quantum resources while maintaining practical relevance. Interoperability between classical pre- and post-processing tools and quantum-specific programming models is paramount for achieving reliable hybrid computation throughput and advancing the state of practical application development.
• Accelerate the maturation of industrial optimization use cases leveraging quantum hardware.
• De-risk commercial adoption pathways by establishing validated performance benchmarks for specific problem classes.
• Translate high-level business objectives into precise quantum-ready graph formulations.
• Reduce the time-to-solution latency for complex computational bottlenecks in client supply chains.
• Advance the technical readiness level (TRL) of quantum computing applications across vertical markets.
• Enable the systematic comparison of quantum versus state-of-the-art classical solver performance.
• Drive the technical relationship required for joint R\&D projects with strategic enterprise partners.
• Document and standardize best practices for running graph optimization problems on QPU infrastructure.
• Inform hardware roadmaps by providing empirical feedback on algorithmic scalability and constraint handling.
• Cultivate a regional talent base capable of deploying hybrid quantum-classical optimization tools.
• Formalize intellectual property via inventive activity in quantum algorithm development and applied math.
• Establish validated software interfaces between industry-specific data sources and quantum programming environments.
Industry Tags: Quantum Optimization, Neutral-Atom Computing, Operations Research, Quantum Algorithms, Applied Mathematics, Graph Theory, Hybrid Computing, Quantum Graph Optimization, High-Performance Computing (HPC)
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